Dynamic scheduling for a scan

ABSTRACT

In an approach for providing adaptive software inventory scan frequencies and schedules, a processor receives information from an initial scan of a set of software inventory scans, wherein the information includes at least one of: running processes, file system entries, registry entries, and software catalog evaluations. A processor analyzes the information from the initial scan. A processor predicts an outcome for future software inventory scans based on the analysis of the information, wherein the prediction includes a scanning frequency and a scanning schedule.

BACKGROUND

The present invention relates generally to the field of software assetmanagement (SAM), and more particularly to providing adaptive softwareinventory scan frequencies and schedules, based on environment dynamics.

SAM is a business practice that involves managing and optimizing thepurchase, deployment, maintenance, utilization, and disposal of softwareapplications within an organization. Fundamentally intended to be partof an organization's information technology (IT) business strategy, thegoals of SAM are to reduce IT costs and limit business and legal riskrelated to the ownership and use of software, while maximizing ITresponsiveness and end-user productivity.

SUMMARY

In one aspect of the present invention, a method, a computer programproduct, and a system includes: (i) receiving, for each machine of aplurality of machines within a networked computing environment, acorresponding initial scan dataset for software inventory scansperformed during a period of time, wherein each corresponding initialdataset includes running processes from usage scans, file system entriesand registry entries from raw scans, software catalog evaluations fromcatalog based scans, and machine parameters of a respectivelycorresponding machine; (ii) analyzing a first initial scan dataset byrunning, by one or more processors, a neural network process using aninput vector having a first scanning frequency and a first scanningschedule; and (iii) predicting, for the respectively correspondingmachine, an outcome for a future usage scan based on the analysis of thefirst initial scan dataset, wherein the outcome includes a secondscanning frequency and a second scanning schedule, and the future usagescan is performed as part of periodic software inventory scans of theplurality of machines.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a block diagram according to an embodiment of the presentinvention.

FIG. 2 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 3 depicts abstraction model layers according to an embodiment ofthe present invention.

FIG. 4 depicts a block diagram of a computing system according to anembodiment of the present invention.

FIG. 5 depicts a flowchart of the steps of a predicting program,executing within the computing system of FIG. 4, for providing adaptivesoftware inventory scan frequencies and schedules, based on environmentdynamics.

DETAILED DESCRIPTION

Currently, software inventory scan frequency and schedule planning isproblematic for Software Asset Management solution administrators.Embodiments of the present invention recognize that there is acompromise between performance/resource consumption and accuracy ofsoftware inventory. Embodiments of the present invention also recognizethat adjustments of set scan schedules are time consuming and difficultto be done correctly in more dynamic environments. Embodiments of thepresent invention provide adaptive software inventory scan frequenciesand schedules, based on environment dynamics.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and predicting program 96.

Referring now to FIG. 4, a diagram of a server 400 in cloud computingnode 10 is shown, in accordance with one embodiment of the presentinvention. FIG. 4 provides an illustration of one embodiment and doesnot imply any limitations with regard to the environments in whichdifferent embodiments can be implemented.

In the depicted embodiment, server 400 contains predicting program 410,database 420, monitoring agent 430, and scheduler 440. Server 400 caninclude components as depicted and described in further detail withrespect to FIG. 1.

Working within workloads layer 90 in FIG. 3, predicting program 410operates to provide adaptive software inventory scan frequencies andschedules, based on environment dynamics. In doing so, predictingprogram 410 receives information from initial scans' results. Predictingprogram 410 analyzes the information from the initial scans' results.Predicting program 410 predicts outcome for future scans. Predictingprogram 410 implements the predictions. Predicting program 410 validatesthe accuracy of the implementation. In the depicted embodiment,predicting program 410 resides on server 400. In other embodiments,predicting program 410, or similar programs, can reside on anotherserver or another computing device, provided that predicting program 410has access to database 420, monitoring agent 430, and scheduler 440.

Database 420 can be a repository that can be written to and/or read bypredicting program 410. In one embodiment, predicting program 410 canallow an administrator to define software inventory scan frequencies andschedules and store the software inventory scan frequencies andschedules to database 420. In some embodiments, database 420 can storesoftware inventory scan frequencies and schedules that are definedwithin predicting program 410. In other embodiments, database 420 may bea knowledge base that is governed by an ontology. A knowledge base is atechnology used to store complex structured and unstructured informationused by a computer system. A knowledge-based system consists of aknowledge base that represents facts and an inference engine that canreason about those facts and use rules and other forms of logic todeduce facts or highlight inconsistencies. In some embodiments,information stored in a knowledge base may include: function categories;descriptions; services; service functions; capabilities andconfigurations; and WordNet (a lexical database for the Englishlanguage). An ontology is a formal, explicit specification of a sharedabstract simplified view of some selected part of the world, containingthe objects, concepts, and other entities that are presumed of interestfor some particular purpose and the relationships between them. In someembodiments, an ontology may use the information stored in a knowledgebase to form and identify the relationships between various scans. Inthe depicted embodiment, database 420 resides on server 400. In otherembodiments, database 420, or similar databases, can reside on anotherserver or another computing device, provided that database 420 isaccessible to predicting program 410.

Monitoring agent 430 is used to monitor machines within an environmentthat needs to be scanned. In one embodiment, monitoring agent 430monitors various machines for a predetermined time period (e.g., onehour, one day, or one week). In other embodiments, monitoring agent 430monitors various machines until predicting program 410 receives an alertthat enough information has been received.

Scheduler 440 is used to schedule a scan. Scheduler 440 is also used toload balance and share system resources effectively or achieve a targetquality of service (overall performance of a computer network).Scheduling is the method by which threads, processes, or data flows aregiven access to system resources—for example, processor time and/orcommunications bandwidth. The need for a scheduling algorithm arisesfrom the requirement for most modern systems to perform multitasking(executing more than one process at a time) and multiplexing(transmitting multiple data streams simultaneously across a singlephysical channel). Scheduler 440 is concerned mainly with throughput,latency, granting equal central processing unit (CPU) time to eachprocess, and the time each process remains in queue. The previouslymentioned concerns often conflict, so scheduler 440 can implement asuitable compromise. The suitable compromise gives preference to any oneof the concerns, depending on a user's needs and objectives. In thedepicted embodiment, scheduler 440 resides on server 400. In otherembodiments, scheduler 440 can reside on another server or anothercomputing device, provided that scheduler 440 is accessible topredicting program 410.

Referring now to FIG. 5, a flowchart of the steps of a predictingprogram is shown, executing within the computing system of FIG. 4, inaccordance with an embodiment of the present invention. Predictingprogram 410 operates to provide adaptive software inventory scanfrequencies and schedules, based on environment dynamics. In someembodiments, the term “dynamic scheduling of a scan” refers to ascheduling of a scan that is characterized by frequent change, activity,or progress. Cloud-based environments are constantly changing;therefore, the scheduling of a scan has to be able to change as well.

In step 510, predicting program 410 receives information from initialscans' results. Software inventory discovery is based on the analysis ofthe results of a “software inventory scan.” However, practically, a“software inventory scan” is a collection of several types of scans,which pull data from different sources from a scanned machine that areexecuted, one by one, at a scheduled time. Examples of scans include,but are not limited to: usage scans—list of running processes; rawscans—file system/registry entries; and catalog based scans—softwarecatalog evaluation.

In one embodiment, predicting program 410 receives information frominitial scans' results via monitoring agent 430. In other embodiments,predicting program 410 uses monitoring agent 430 to monitor machines fora predetermined time period (e.g., one hour, one day, or one week). Insome embodiments, predicting program 410 uses monitoring agent 430 tomonitor machines until predicting program 410 receives an alert thatenough information has been received.

Embodiments of the present invention provide adaptive software inventoryscan frequencies and schedules for different types of scans, based onenvironment dynamics, machine characteristics, and scannersinterdependencies relevant for scanned machines. Predicting program 410allows the frequency and schedule of a scan to adapt to specificmachines, which depend on given factors and parameters. Additionally,predicting program 410 provides automated schedule time adjustments persource scan and keeps a dynamic balance between software inventoryaccuracy and resource consumption. Predicting program 410 utilizesneural network algorithms for determining software scan frequencies andschedules for various types of scans. In some embodiments, predictingprogram 410 uses existing initial scans' results for self-learning ofcustomer environment specifics and adaptive evaluation of the scheduleof future scans.

The neural network is trained with examples of software scan frequenciesand schedules. In machine learning and cognitive science, artificialneural networks are a family of models inspired by biological neuralnetworks and are used to estimate or approximate functions that candepend on a large number of inputs and are generally unknown. In oneembodiment, predicting program 410 retrieves already existing initialscans' results with collected machine characteristics and scannersinterdependencies from database 420. In other embodiments, predictingprogram 410 receives initial scans' results with collected machinecharacteristics and scanners interdependencies in real-time. A typicalsoftware scan schedule is made for each machine group based on location,operating system, or specification of computer, so an input vector forthe neural network could be constructed as shown in Table 1.

TABLE 1 Example construction of an input vector for the neural network.Data Position in Vector Scanner type ID 1 Schedule week of month 2Schedule day of week 3 Schedule hour 4 Agent name represented by ASCIInumbers  2-61 and filled with 0 to have common length Agent IP 62-65Agent OS represented by ASCII numbers and  66-125 filled with 0 to havecommon length CPU speed in MPS 126 RAM capacity in MB 127 Disc capacityin MB 128

In step 520, predicting program 410 analyzes the information from theinitial scans' results. In one embodiment, predicting program 410analyzes the information from the initial scans' results using vectorsthat contain the results from the initial scans, wherein each of suchvectors in the learning phase will be labeled with scan results delta,discovered software delta, and CPU, random-access memory (RAM), and discusage by scanning process. The vector will represent the outcome of theperformed scan, which should balance between software inventory accuracyand resource consumption as shown in Table 2.

TABLE 2 An example vector representing the outcome of the performedscan. Data Position in vector Scan Delta comparison of the last and 1previous scan results, provided in % Software Delta comparison ofdiscovered 2 software before and after the last scan, provided in % ScanDuration 3 CPU Usage average CPU utilization by 4 scanner in % of totalcapacity RAM Usage average RAM utilization by 5 scanner in % of totalcapacity Disc Usage average disc consumption by 6 scanner in % of totalsize

In step 530, predicting program 410 predicts the outcome for futurescans. As a result of the learning phase, predicting program 410 obtainsneural network parameters and is able to predict the outcome for aproposed scan type, schedule, and target machine characteristics. In oneembodiment, predicting program 410 predicts the outcome for future scansby using the received neural network parameters in real-time. In otherembodiments, predicting program 410 predicts the outcome for futurescans by using neural network parameters retrieved from database 420.

In step 540, predicting program 410 implements the predictions. In oneembodiment, predicting program 410 implements the predictions inscheduler 440, which sets further scans frequency and schedule forsystems. In other embodiments, predicting program 410 implements thepredictions based on the outcome of previous learned scans. In someembodiments, the algorithm adapts to newly added systems, which wouldhave characteristics in common with other machines.

In step 550, predicting program 410 validates the accuracy of theimplementation. In one embodiment, predicting program 410 collects scanfrequencies and schedules and stores the collected scan frequencies andschedules to database 420. In other embodiments, predicting program 410validates the accuracy of the implementation, based on the collectedscan frequencies and schedules in real-time. In some embodiments,predicting program 410 validates the accuracy of the implementation,based on retrieving scan frequencies and schedules from database 420.Still, in other embodiments, there is a predefined threshold that has tobe crossed before predicting program 410 reiterates the learning phasewith all of the latest scan results. An example of a predefinedthreshold is a scan duration that lasts one minute. In such anembodiment, when the outcome of a scan is more than one minute,predicting program 410 reiterates the learning phase with all of thelatest scan results.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The terminology used herein was chosen to best explain the principles ofthe embodiment, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A computer-implemented method for adaptingsoftware inventory scans to predicted machine parameters, the methodcomprising: receiving, for each machine of a plurality of machineswithin a networked computing environment, a corresponding initial scandataset for software inventory scans performed during a period of time,wherein each corresponding initial dataset includes running processesfrom usage scans, file system entries and registry entries from rawscans, software catalog evaluations from catalog based scans, andmachine parameters of a respectively corresponding machine; analyzing afirst initial scan dataset by running, by one or more processors, aneural network process using an input vector having a first scanningfrequency and a first scanning schedule; and predicting, for therespectively corresponding machine, an outcome for a future usage scanbased on the analysis of the first initial scan dataset, wherein theoutcome includes a second scanning frequency and a second scanningschedule, and the future usage scan is performed as part of periodicsoftware inventory scans of the plurality of machines.
 2. The method ofclaim 1, further comprising: implementing the outcome for the periodicsoftware inventory scans.
 3. The method of claim 2, wherein implementingthe outcome for the periodic software inventory scans comprises:generating a vector representing the outcome including measurements of ascan duration, a system utilization of a scanning process, and aresulting set of identified software programs of the scanning process.4. The method of claim 2, further comprising: causing, by one or moreprocessors, a first periodic software inventory scan to be performedsubsequent to implementing the outcome; and validating an accuracy ofthe first periodic software inventory scan, based on the outcome.
 5. Themethod of claim 4, wherein validating the accuracy of the first softwareinventory scan comprises: validating an accuracy of implementing theoutcome for the periodic software inventory scans, based on a predefinedthreshold, wherein the predefined threshold includes a usage scanduration.
 6. The method of claim 1, further comprising: analyzing eachcorresponding initial scan dataset for customer environment information;wherein: predicting the outcome for a future usage scan is further basedon the customer environment information.
 7. A computer program productfor adapting software inventory scans to predicted machine parameters,the computer program product comprising: one or more computer readablestorage media and program instructions stored on the one or morecomputer readable storage media, the program instructions comprising:program instructions to receive, for each machine of a plurality ofmachines within a networked computing environment, a correspondinginitial scan dataset for software inventory scans performed during aperiod of time, wherein each corresponding initial dataset includesrunning processes from usage scans, file system entries and registryentries from raw scans, software catalog evaluations from catalog basedscans, and machine parameters of a respectively corresponding machine;program instructions to analyze a first initial scan dataset by running,by one or more processors, a neural network process using an inputvector having a scanning process frequency and a scanning processschedule; and program instructions to predict, for the respectivelycorresponding machine, an outcome for a future usage scan based on theanalysis of the first initial scan dataset, wherein the outcome includesa scanning frequency and a scanning schedule, and the future usage scanis performed as part of periodic software inventory scans of theplurality of machines.
 8. The computer program product of claim 7, theprogram instructions further comprising: program instructions toimplement the outcome for the periodic software inventory scans.
 9. Thecomputer program product of claim 8, wherein program instructions toimplement the outcome for the periodic software inventory scanscomprise: program instructions to generate a vector representing theoutcome including measurements of a scan duration, a system utilizationof a scanning process, and a resulting set of identified softwareprograms of the scanning process.
 10. The computer program product ofclaim 8, the program instructions further comprising: programinstructions to cause a first periodic software inventory scan to beperformed subsequent to implementing the outcome; and programinstructions to validate an accuracy of the first periodic softwareinventory scan, based on the outcome.
 11. The computer program productof claim 10, wherein program instructions to validate the accuracy ofthe first software inventory scan comprise: program instructions tovalidate an accuracy of implementing the outcome for the periodicsoftware inventory scans, based on a predefined threshold, wherein thepredefined threshold includes a usage scan duration.
 12. The computerprogram product of claim 7, the program instructions further comprising:program instructions to analyze each corresponding initial scan datasetfor customer environment information; wherein: predicting the outcomefor a future usage scan is further based on the customer environmentinformation.
 13. A computer system for adapting software inventory scansto predicted machine parameters, the computer system comprising: one ormore computer processors, one or more computer readable storage media,and program instructions stored on the computer readable storage mediafor execution by at least one of the one or more processors, the programinstructions comprising: program instructions to receive, for eachmachine of a plurality of machines within a networked computingenvironment, a corresponding initial scan dataset for software inventoryscans performed during a period of time, wherein each correspondinginitial dataset includes running processes from usage scans, file systementries and registry entries from raw scans, software catalogevaluations from catalog based scans, and machine parameters of arespectively corresponding machine; program instructions to analyze afirst initial scan dataset by running, by one or more processors, aneural network process using an input vector having a scanning processfrequency and a scanning process schedule; and program instructions topredict, for the respectively corresponding machine, an outcome for afuture usage scan based on the analysis of the first initial scandataset, wherein the outcome includes a scanning frequency and ascanning schedule, and the future usage scan is performed as part ofperiodic software inventory scans of the plurality of machines.
 14. Thecomputer system of claim 13, the program instructions furthercomprising: program instructions to implement the outcome for theperiodic software inventory scans.
 15. The computer system of claim 14,wherein program instructions to implement the outcome for the periodicsoftware inventory scans comprise: program instructions to generate avector representing the outcome including measurements of a scanduration, a system utilization of a scanning process, and a resultingset of identified software programs of the scanning process.
 16. Thecomputer system of claim 14, the program instructions furthercomprising: program instructions to cause a first periodic softwareinventory scan to be performed subsequent to implementing the outcome;and program instructions to validate an accuracy of the first periodicsoftware inventory scan, based on the outcome.
 17. The computer systemof claim 16, wherein program instructions to validate the accuracy ofthe first software inventory scan comprise: program instructions tovalidate an accuracy of implementing the outcome for the periodicsoftware inventory scans, based on a predefined threshold, wherein thepredefined threshold includes a usage scan duration.
 18. The computersystem of claim 13, the program instructions further comprising: programinstructions to analyze each corresponding initial scan dataset forcustomer environment information; wherein: predicting the outcome for afuture usage scan is further based on the customer environmentinformation.